large samples.

With access to large data sets, we can run standard statistical tests with much large sample sizes than ever before.

When this happens, it becomes really easy to have a very low p-value. Forget 0.05, we're talking 0.00001.

A more interesting question becomes: Is the difference large enough to be interesting?

The Job Hunt.

Something obvious occurred to me. The point of any job hunt is to find a new local maximum.

Here's the claim: With each job and payrate, there's a certain set-point of happiness that the job returns to. Get a pay raise, and that set point goes up -- but less than we'd like to think.

But, get a job doing something you find more important, or just more rewarding, and the set point goes up a lot.

And here's when you should change jobs: When the set point for your current job is considerably lower than where you want to be.

I've got a chart for that. This chart shows the original happiness at a job, and what happens when reality hits. A pay raise goes along with increase in happiness, as does additional responsibilities.

Here's the problem: At least in this example, the happiness increases in the position have failed to keep up with the desired satisfaction.

There are two ways out of this: change a job or lower expectations.

Which do you think should happen?

Teaching Night School.

It is official: If I finish the paperwork, I'll be teaching night classes in October!

While they are going to pay me for this, I consider this to be largely volunteer work. It is for the Arlington Public Schools Adult Education, and it is largely open to anyone who can pay. That price is:
$109 Arlington Residents
$85 Arlington Seniors
$145 non-Arlington Resident
$109 Non-Resident Senior

The course is entitled Excel: Beyond The Basics. According to the course description I've been handed, it will cover:

--Named Ranges.
--Conditional formatting
--Logic Functions to test data
--Comments
--External Data

This needs to happen in 3 3-hour session.

I am almost certainly going to wind up tailoring from the podium. With a 10 minute break each session, 10 minutes to warm up and cool off each time, and a half hour of introductions in the first session, I've got 7 hours. That's about 84 minutes per topic.

Here's the plan:

---- Named Ranges ----
If I'm doing named Ranges in 80 minutes, I will probably cover 2 ways to create them, explain the Named Ranged Edit Box, and show them used in a few formulas. Then give a 20 minute exercise where they create a named range and use it in a formula. We'll then do a second formula. Then we update the named range, and show how it cascades through each formula.


---- Conditional Formatting ---
As for conditional formatting, I'll start with "wouldn't it be nice if negative numbers could easily came up in red?" -- and yes, i know this is doable through other means. But, if we allow the zero to be arbitrary, it becomes much more useful.

For instance, when measuring out my monthly spending, I want Excel to tell me in RED if my checking balance will ever be below a thousand bucks.

---- Logical Functions To Test Data ---
Here's the real trick: We just did this in "Conditional Formatting". This'll be a lot of 'if my outgoing money is bigger than my income revenue, what happens?'

I'm liable to find or create a decent data set and do some fun work with "and" and "0r". I'll avoid NAND.

---- Comments ---
the most useful thing comments can do for is is the same thing they do in code: Tell a future version of yourself or other programming what the hell you were talking about. I'll discuss this. Then I'll pull up a spreadsheet without any comments and ask them what it means. Then I'll pull up a decently commented version, and it'll be a lot easier to figure out.

---- External Data ---
We may do a web query, at least if there is active internet access. Otherwise, I'll bring in a comma-delimited file and we can figure it out.

---- Pivot Tables ---
If we have any time left, I'm liable to go back to the well and discuss pivot tables. We'll talk about the utility of pivotting data, and why it matters. I'll use a data set -- possibly a credit card statement -- with a few hundred rows. Then, we pivot the data and some things become almost immediately obvious.

Speed of thought visual data analysis. Awesome.

So, that's my plan to teach Excel to a population that shows up and wants to improve themselves.

This is a plan in progress, and I expect it to be modified as time goes on. I also expect I will wind up doing some tailing from the podium to bring the course into line with the expectations of the students. I hope they feel comfortable telling me if they have stopped following, or if I am going to slow. To ensure that, I'll need to establish trust in the beginning.

My guess is credibility will be easy to establish with this crowd, but trust a lot harder than with the government analysts I've been training.

I think I'll do that by:
1) Not stressing the fancy degrees, but mentioning.
2) Making intentional mistakes.
3) Asking everyone what their expectations are, and constant checkins on how they feel.
4) Dressing less fancy than when I deliver for the company. I'm thinking sandals plus business casual.

If I manage all that, I should be able to establish credibility, maintain trust and ensure attention.

Responsibilities.

I work for a training firm. What we do is assist employees, usually federal ones, in developing skills, both for the job and --- often -- skills that are useful in other ways.

Over the last year my responsibilities have grown tremendously.

In May, I taught my first course, a 3-day with 5 participants. In June, I taught a 5-day course with neither backup nor a safety net. There were 18 people in the course. In July, I created the majority of a new course. It was a major rewrite of some curriculum, and I won most of the fights I was really interested in. Two weeks ago, my boss put me on call while he was teaching a class in case he needed to attend to family matters.

In August, I'm teaching 2 courses. There is one work day between the two. This will be a total of four classes taught this summer.

Last summer, working for the same company, I wrote some exercises and took a course from another division of our company.

This is a fairly sizable difference in responsibilities. I've become a trusted member of the workforce, and am directly interacting with our clients and representing the company.

If that's what a year has done, I wonder what another year will do?

On the nature of clothes not fitting.

I have difficulty buying suits. Apparently the ratio of my waist to my shoulders is a little off from the "standard". This results in a few problems that invariably cost me additional money.

My understanding is women have it a lot worse, and that it can be a challenge for many women to find clothes in stores at all.

I don't know how this is possible. Imagine going to a car dealer and not being able to fit into a car. Or to Chipotle and being unable to eat rice.

There are a few potential solutions. One is sites like etsy, or other ways of having clothes custom made. This need not be expensive, but it isn't a system-wide solution.

Instead, I'd to recommend a 2-sigma solution. Imagine a starting clothing company that has decided that 95% of the population will be able to find clothes off the rack. Assuming that the shape of the population is roughly normal, how would this be possible?

A few steps:
1. Take a sample of the population. Use about 385 individuals per sample, and try to reduce bias in the sample.
2. Measure everything about these folks. Inseam, waist, distance from waist to armpit, length of arm. Get the ratios. Figure everything out you need to make them clothes.
3. Repeat steps (1) and (2) a few times. Graph the points, and look for differences. From these multiple samples, create a 95% confidence interval.

Now you've measured a small sample of the population, and have a pretty decent interval measure. For optimal results, take measurements each season. Then not only do you know what clothes are needed, but you also know the changing shape of the population.

My guess is that I'm well within 2 sigma, and that many clothing stores simply do not cater to that wide of margins of the population.

The first company to do so should make a killing. The second to do so is liable to kill the first.

Being Appreciated.

I've now been in the workforce for a year.

I've been taking on more responsibility.

Official as of mid-June, I'm making a significant amount more than I was hired at.

Still less than my loans, but a nice raise and -- technically -- a promotion.

I'm going to be doing the exact same job, but my title is going from "Analyst" to "Senior Analyst."

My salary is increasing by a little bit more than 5% over the company-wide 3% we had earlier this year.

Its a little more than 5% as, instead of increasing by a percentage, the raise goes to a round number that is close to the percentage.

It is good to be appreciated for my successes.

Next week: I'm delivering a 5-day, 40-hour behemoth of a course that we just rewrote.

The Economics of Moving.

I've been moving in the past month. This should due a large part to explain the lack of posting. Moving furniture is time consuming and expensive.

Due to this, I've been thinking about the payback period on the amount I've expended to move. While there are a lot of quality of life differences (such as being a lot closer to Dianne), that only comes off in derived statistics.

Let's see what we can tell.

Previous expenses:
Rent: 1200
Groceries: The occasional trip to the store. If alone, it would be crap. If with Dianne, we'd go find stuff 3 or so times a week. My average monthly grocery store bill for the last year is $230. The above chart shows this spending pattern, if it can be called a pattern.
Electricity: Included in rent.
Internet: Municipal wifi that was unreliable.
Netflix: We used Dianne's, and I mooched. Free.
Total Monthly Expenses: Call it 1430.

Now:
Rent: 800
Groceries: We're getting $50 of groceries delivered each week, of which I'm paying half. I expect this to reduce my grocery store bill by at least half. Call it $100 plus the $100 of delivered groceries. $200 / month.
Internet: We're splitting FiOS, which should run around $30 each.
Netflix: We're upping to 3, and I'm going to pay half. Call it $10 a month.
Electricity: ~$25/month
ZipCar: $15
Total Expenses: $1080 / month

That is, each month I expect to save nearly $350 over how much I was spending.

However, merely moving is pretty expensive. We both paid an extra month's rent so that we didn't need to do it over a weekend. There's also the expense of needing to eat out more often, but my grocery store spending is so varied that I don't feel right making a guess as to how much I didn't spend there.

Even ignoring that, I've spent an extra $1200 this month for double rent. If that's everything, then the question becomes:

How long will it take me to repay the $1200 I spent moving, if I'm saving $350 each month?

The answer to this question is pretty obvious -- a little under 4 months -- and tells me that the move is cost effective, if we look at any considerable length of time. Certainly over the course of a year it will be incredibly effective.

My next question is: When do I reach my savings goals?
First goal: One months set expenses. The amount needed went down. I now have, in checking, enough for everything that comes through my checking account. Win.
Second Goal: 3 months expenses. This is a little harder. My spending over the last year has been 20546.58. However, this includes some outliers -- like purchasing all my furniture in one month. The giant peak on the chart is the Ikea trip.

If we ignore the months where I spent furniture, the average is around $1500. That includes the grocery store and other expenses that should be going down, so let's call it $1000.

To have 3 months expenses, I need about $6000.

When I started this job, I had $6000 and could last 12 months on it. That's lifestyle inflation.

So, this question reduces to the fairly simple: If I'm saving 350 dollars a month and already have a thousand dollars, how long will it take me to get to a total of $6000?

The answer is, of course, 14 months and about a week.

That's not so bad, but here's hoping I get a raise.